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Inrush current occurrence in the power transformers is an unavoidable event that can happen due to energizing no-load transformer. Although various protection methods based on the terminal current and/or voltage have been proposed for transformer protection, the protection malfunction caused by the inrush current still occurs occasionally. In this paper, By comparing different closing angles of transformers with normal condition and differences in leakage flux near the yoke, a simple and accurate method is presented to identify transformer inrush current conditions. The change of the flux density on the leakage flux sensor can effectively identify the transformer inrush current. This identification method has been initially verified in the simulation in Maxwell.

Systematic identification of protein complexes is an important application of data mining in bioscience. The existing computation methods of detecting protein complexes are usually based on the topological properties of protein-protein interaction(PPI) network data. However, limited by the inherent single structure of the PPI network, the mining of protein complexes may not be fully resolved. In this paper, we propose an original multi-objective optimization strategy based protein complex detection method, which is used Adaptive Multi-Objective Black Hole algorithm, called AMOBH. In the selection of objective function strategy, we integrate the topological structure of PPI network data with semantic similarity based on GO (Gene Ontology) annotation data. Experiments demonstrate that the method we adopt provide more convinced results and higher computational efficiency compared with the existing prediction measures.

Computational methods are often applied to identify essential proteins from protein-protein interaction networks. In this paper, inspected by node and edge clustering coefficient (NEC) and PeC, we propose an improved version of node and edge clustering coefficient (INEC) which both considers dual topological characteristics of the network and high false positives of the protein-protein interaction data. We apply it for the identification of essential proteins. And we implement three versions of INEC which combine different biological information. In order not to be confused, we call the first one INEC0 which dosen't integrate biological information, the second one INEC1 which integrates gene expression similarity, and the third one INEC2 which integrates gene expression similarity, functional similarity, and protein-protein sequence similarity. We apply three implemented INEC methods to protein-protein interaction data of Saccharomyces cerevisiae (Yeast) and compare them with some state-of-the-art methods (DC, NC, PeC, and NEC). The experimental results show that our proposed methods achieve better results in terms of prediction accuracy, area under the curve of PR-curve, and Jackknife methodology.

Internet users have to face to tremendous information from website. Clustering is a good solution to organize information. However, most clustering algorithms operate in the static situation. That means, it doesn't allow any incremental data. Certainly, this restrict is not fit to network environment, since data from internet is continuous increasing. Thus, an incremental clustering algorithm based on self-organizing-mapping is proposed in this paper. This algorithm has two innovations. (a) It integrates feature's ability in similarity measurement. (b) Based on the devised similarity measurement, this algorithm selects few samples from original texts to perform incremental clustering. Experiments demonstrate that, after integrating feature's ability in similarity measurement, our algorithm can obtain high clustering performance.

As the renewable energy in microgrids exhibits inherent intermittence and volatility, their forecasts are subject to a high level of uncertainty even for a few hours. To deal with the uncertainty in microgrids, new energy management strategies are necessary to be developed. This paper presents a strategy to optimally schedule and operate a microgrid while mitigating the impact of the uncertainty. Uncertainty in load, and renewable energy such as wind and photovoltaic systems is considered in this paper. The load minus the power output of renewable energy is viewed as equivalent load, whose distribution is described as a forecast range at a given confidence level. The forecast range is divided into several sub-ranges, and each sub-range is assigned a schedule by performing an optimization. In this way, corresponding scheme is implemented when the real value falls into the sub-range at real-time operation. The proposed strategy is tested on a radical microgrid. Numerical simulation indicates that the strategy is effective to produce an accurate generation schedule while maintaining an economic performance of the system. In addition, it is able to reduce the uncertainty, which lowers the reserve requirement for compensating the deviations and reduces the risk of overcharging and overdischarging of energy storage systems.

Three-phase PWM rectifier is one of the core techniques applied in large scale wind power system. DC-bus voltage usually fluctuates as the variation of load and ac voltage, which may hurt the safety and reliability of the system. In order to restrain dc-bus voltage fluctuation and improve system dynamic response, a direct current control and dual close loop control structure in d-q synchronous reference frame are employed. By analyzing the factors which influence the fast transient response, an optimum feed-forward control strategy is proposed based on the power balance relationship between the input and output of rectifier. With this control, the dc-bus voltage is immunized from not only variation of load but also variation of grid voltage, and the capacitance of the dc link capacitors can be reduced at same transient response demand of system. Simulation results show that the proposed control scheme has satisfactory steady-state characteristics and fast transient response.